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Found 35 papers, 8 papers with code
Co-investment with Payoff Sharing Benefit Operators and Users in Network Design
In this paper, we consider the question of what cooperative mechanisms can benefit both operators and users simultaneously.
Reliability-aware Control of Power Converters in Mobility Applications
This paper introduces an automatic control method designed to enhance the operation of electric vehicles, besides the speed tracking objectives, by including reliability and lifetime requirements.
A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness
Autonomous Vehicles (AVs) promise a range of societal advantages, including broader access to mobility, reduced road accidents, and enhanced transportation efficiency.
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes
Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique.
Factorization of Multi-Agent Sampling-Based Motion Planning
Although standard Sampling-based Algorithms (SBAs) can be used to search for solutions in the robots' joint space, this approach quickly becomes computationally intractable as the number of agents increases.
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games
We consider the interaction among agents engaging in a driving task and we model it as general-sum game.
CARMA: Fair and efficient bottleneck congestion management via non-tradable karma credits
In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion.
Compositional Controller Synthesis for Interconnected Stochastic Systems with Markovian Switching
We apply our results to a room temperature network of 200 rooms with Markovian switching signals while accepting multiple storage certificates.
Safety Barrier Certificates for Stochastic Hybrid Systems
In our proposed scheme, we first provide an augmented framework to characterize each stochastic hybrid system containing continuous evolutions and instantaneous jumps with a unified system covering both scenarios.
A self-contained karma economy for the dynamic allocation of common resources
This paper presents karma mechanisms, a novel approach to the repeated allocation of a scarce resource among competing agents over an infinite time.
Constructing MDP Abstractions Using Data with Formal Guarantees
We then propose a scenario convex program (SCP) associated to the original RCP by collecting a finite number of data from trajectories of the system.
Data-Driven Synthesis of Symbolic Abstractions with Guaranteed Confidence
In this work, we propose a data-driven approach for the construction of finite abstractions (a. k. a., symbolic models) for discrete-time deterministic control systems with unknown dynamics.
Task-driven Modular Co-design of Vehicle Control Systems
When designing autonomous systems, we need to consider multiple trade-offs at various abstraction levels, and the choices of single (hardware and software) components need to be studied jointly.
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach
The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation.
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning
In particular, we show that, when using weight decay, weights in successive layers of a deep network may become "mutually frozen".
Analysis and Control of Autonomous Mobility-on-Demand Systems
We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems.
Rule-based Optimal Control for Autonomous Driving
We develop optimal control strategies for Autonomous Vehicles (AVs) that are required to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior.
Autonomous Driving
Robotics
Systems and Control
Systems and Control
The Negative Pretraining Effect in Sequential Deep Learning and Three Ways to Fix It
Negative pretraining is a prominent sequential learning effect of neural networks where a pretrained model obtains a worse generalization performance than a model that is trained from scratch when either are trained on a target task.
Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
In this work, we consider the problem of co-designing the control algorithm as well as the platform around it.
Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.
Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions
This requires tools to study such a coupling and co-design mobility systems in terms of different objectives.
A Compositional Sheaf-Theoretic Framework for Event-Based Systems (Extended Version)
A compositional sheaf-theoretic framework for the modeling of complex event-based systems is presented.
Quantifying the effect of representations on task complexity
To quantify this alignment effect of data representations on the difficulty of a learning task, we make use of an existing task complexity score and show its connection to the representation-dependent information coding length of the input.
The Frechet Distance of training and test distribution predicts the generalization gap
Empirically across several data domains, we substantiate this viewpoint by showing that test performance correlates strongly with the distance in data distributions between training and test set.
Today Me, Tomorrow Thee: Efficient Resource Allocation in Competitive Settings using Karma Games
We present a new type of coordination mechanism among multiple agents for the allocation of a finite resource, such as the allocation of time slots for passing an intersection.
Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks
We define a "rulebook" as a pre-ordered set of "rules", each akin to a violation metric on the possible outcomes ("realizations").
Landmark Guided Probabilistic Roadmap Queries
A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method.
Set-Point Regulation of Linear Continuous-Time Systems using Neuromorphic Vision Sensors
Recently developed neuromorphic vision sensors have become promising candidates for agile and autonomous robotic applications primarily due to, in particular, their high temporal resolution and low latency.
A Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning
A resolution complete optimal kinodynamic motion planning algorithm is presented and described as a generalized label correcting (GLC) method.
Robotics
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation.
Robotics
POMDP-lite for Robust Robot Planning under Uncertainty
We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks.
Free-configuration Biased Sampling for Motion Planning: Errata
This document contains improved and updated proofs of convergence for the sampling method presented in our paper "Free-configuration Biased Sampling for Motion Planning".
Fast Collision Checking: From Single Robots to Multi-Robot Teams
We examine three different algorithms that enable the collision certificate method from [Bialkowski, et al.] to handle the case of a centralized multi-robot team.
Sampling-based Algorithms for Optimal Motion Planning
The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i. e., such that the cost of the returned solution converges almost surely to the optimum.
Robotics
Incremental Sampling-based Algorithms for Optimal Motion Planning
Second, a new algorithm is considered, called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost of the best path in the RRG converges to the optimum almost surely.
Robotics 68T40
